A population-based study to assess two convolutional neural networks for dental age estimation.

ABSTRACT:

Background

Dental age (DA) estimation using two convolutional neural networks (CNNs), VGG16 and ResNet101, remains unexplored. In this study, we aimed to investigate the possibility of using artificial intelligence-based methods in an eastern Chinese population.

Methods

A total of 9586 orthopantomograms (OPGs) (4054 boys and 5532 girls) of the Chinese Han population aged from 6 to 20 years were collected. DAs were automatically calculated using the two CNN model strategies. Accuracy, recall, precision, and F1 score of the models were used to evaluate VGG16 and ResNet101 for age estimation. An age threshold was also employed to evaluate the two CNN models.

Results

The VGG16 network outperformed the ResNet101 network in terms of prediction performance. However, the mode

SUBMITTER: Wang J 

PROVIDER: S-EPMC9938587 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature